Control of wind turbines

ABSTRACT

A wind turbine power plant comprises a plurality of wind turbines, each having a rated output and under the control of a power plant controller. The power plant also has a rated output which may be over-rated in response to one or more of electricity pricing data, power plant age and operator demand. The power plant controller can send over-rating demand signals to individual turbines. The controllers at the turbines include a fatigue life usage estimator which estimates a measure of the fatigue life consumed by key components of the turbine. If this measure exceeds a target value for any component, over-rating is prevented at that turbine.

This invention relates to control of wind turbines and wind power plantsand, in particular to control methods and apparatus which take intoaccount the condition of the wind turbine when making control decisions.

The rated power of a wind turbine is defined in IEC 61400 as the maximumcontinuous electrical power output which a wind turbine is designed toachieve under normal operating and external conditions. Large commercialwind turbines are generally designed for a lifetime of 20 years andtheir rated power output takes into account that lifespan.

Wind turbines are commonly operated as part of a wind power plantcomprising a plurality of wind turbines. U.S. Pat. No. 6,724,097discloses operation of such a wind plant. The output of each turbine isdetermined and one or more turbines controlled so that the output powerof one or more turbines is reduced if the total output exceeds the ratedoutput of the plant. Such an arrangement is useful as the sum of theindividual rated powers may exceed the rated output of the wind powerplant, but at any one time not all turbines may be operating at fullcapacity; some may be shut down for maintenance and some may beexperiencing less than ideal wind conditions.

While the approach taken in U.S. Pat. No. 6,724,097 deals with avoidingoverproduction by a wind power plant, the total output of the plant maynot reach the rated plant power if some turbines are shut down, forexample for maintenance, or are not operating at their rated power, forexample because the local wind conditions at those turbines do not allowrated power output to be achieved. It is economically desirable,therefore, to boost the output of one or more of the turbines toincrease the total output of the power plant to its rated output.However, such boosting risks damaging the turbines.

U.S. Pat. No. 6,850,821 discloses a wind turbine controller that hasmeasured stress conditions as an input allowing it to control the outputpower as a function of measured stress. Thus, for example, power outputmay be reduced in very turbulent wind conditions in comparison to lessturbulent conditions having the same average wind speed.US-A-2006/0273595 discloses intermittently operating a wind power plantat an increased rated power output based on an assessment of operatingparameters with respect to component design ratings and intermittentlyincreasing the output power of a wind turbine based on the assessment.The present invention aims to provide improved methods and apparatus forcontrolling wind turbines.

According to the invention there is provided a controller for a windturbine, comprising a turbine optimiser and a lifetime usage estimator,the turbine optimiser outputting setpoints for operating parameters ofthe wind turbine based on a power demand input and an input from thelifetime usage estimator, wherein the lifetime usage estimatorcalculates a measure of the fatigue life consumed by each of a pluralityof turbine components based on a lifetime usage algorithm for eachcomponent, the lifetime usage algorithms operating on values ofvariables affecting the fatigue lifetime of the components, the valuesbeing obtained from or derived from sensors on the wind turbine.

The invention also provides a method of controlling a wind turbine,comprising obtaining values of variables affecting the fatigue lifetimeof one or more components of the turbine from turbine sensors, applyinga fatigue lifetime usage algorithm to the variables to determine ameasure of the fatigue life consumed by each of a plurality of turbinecomponents, inputting the measures of fatigue life consumption and apower demand to a turbine optimiser, and generating setpoints foroperating parameters of the wind turbine by the turbine optimiser.

The invention further provides a method of over-rating a wind turbinecomprising receiving an over-rating demand signal form a power plantcontroller, determining a measure of the fatigue life consumed by eachof a plurality of turbine components based on a lifetime usage algorithmfor each component and sensed parameter values for that component,generating and sending to the turbine at least one of a power a torqueset point in accordance with the over-rating demand set point, whereinthe over-rating set points are not sent to the turbine if the measure ofthe fatigue life consumed exceeds a target value for a component.

Embodiments of the invention have the advantage that setpoint signalssuch as power and torque are conditional on the estimated condition ofturbine components. The fatigue life usage of key components can beestimated from sensed parameters or parameters derived from sensedparameters in conjunction with an appropriate fatigue lifetime usagealgorithm. Once the fatigue lifetime usage has been calculated it can becompared with the target lifetime usage of components, based for exampleon the time since commissioning. If the estimated fatigue life usage ofany component is above the target, over-rating of the turbine may besuppressed. This enables over-rating to be achieved without the risk ofdamage to turbine components and without the risk of shortening the lifeof components which might counteract the financial benefits ofover-rating.

In one embodiment of the invention, the setpoints output by the turbineoptimiser are torque and speed setpoints. The input from the lifetimeusage estimator may comprise one or both of a measurement of fatiguelife consumed by each component and a measurement of rate of usage offatigue life by each component. The measurement of fatigue life usageenables a determination of the overall condition of a component to bemade whereas the measurement of the rate of fatigue rate usage enablesover-rating to be prevented if the fatigue life is being consumedquickly, even if the current fatigue life is less than the target atthat time.

Measurement of the rate of change of life used may also enable theidentification of the influence of Low-Cycle Fatigue (LCF), since thecompletion of each single cycle of large and long-duration stress orstrain will result in a sudden large change in lifetime used, which canbe detected as an event and used to moderate over-rating in responsethereto.

In one embodiment the lifetime usage calculations are implemented on aturbine that has already been in service and recorded historical data ofthe turbine's operation. The historical data may then be used by meansof appropriate calculations to set the initial values for the lifetimeusage estimators and to set the initial strategy for the over-ratingcontrol. By setting the initial values the need to operate the turbinewith lifetime usage calculations for an initial period, typically 1-yearof operation, before an over-rating control strategy can be implementedmay be avoided.

In one embodiment of the invention the turbine optimiser compares theproportion of the fatigue life consumed by the components with a targetconsumption based on the age of the component and prevents over-ratingof the turbine if the fatigue life consumed by any component is greaterthan the target consumption for that component. In another embodimentthe turbine optimiser compares the proportion of the fatigue lifeconsumed by the most damaged components with a target consumption basedon the age of that component and prevents over-rating of the turbine ifthe fatigue life consumed is greater than the target consumption forthat component.

In one embodiment, the over-rating comprises speed and torqueover-rating and wherein the fatigue life of each component is speedsensitive, torque sensitive or speed and torque sensitive, whereinover-rating of the speed is prohibited if the consumption of fatiguelife by a speed sensitive component exceeds the target consumption,over-rating of torque is prohibited if the consumption of fatigue lifeby a torque sensitive component exceeds the target consumption andover-rating of speed and torque is prohibited if the consumption offatigue life by a torque and speed component exceeds the idealconsumption. Thus, over-rating is only prohibited if a given componentis above its target fatigue life and is sensitive to the parameter thatis being over-rated.

In one embodiment the turbine optimiser communicates with a wind powerplant controller and receives the power demand input from the wind powerplant controller. The controller may be located at the wind turbine forwhich it generates setpoint signals. The controller may comprise a storestoring a library of lifetime usage algorithms, wherein the lifetimeusage of each of the components is calculated using at least one of thealgorithms. Such an arrangement is advantageous as it enables newalgorithms to be added to the library to estimate fatigue life usage foradditional components of to provide a new algorithm for existingcomponents.

The turbine components the fatigue life of which is estimated may be oneor more of blade components, bearing components, blade pitch controlcomponents, main shaft, gearbox, generator, converter, transformer, yawsystem, tower or foundation. The blade components may comprise bladestructure, blade bearings and blade bolts.

In one embodiment, the lifetime usage algorithm for a componentestimates a stress cycle range and mean value based on received inputvalues. The stress cycle and mean values may be inputs to a stress cycledamage algorithm which provides as its output, the measure onconsumption of fatigue life.

In one embodiment, the turbine optimiser receives an input ofoperational constraints indicating restrictions on operating parametersof the turbine. The turbine optimiser may comprise a set point selectorwhich receives the power demand input and the measure of lifetimeconsumed for the components and periodically calculates optimaloperating set points for the turbine parameters, and a constraint unitwhich receives the optimal set points, the measure of lifetime usage andthe power demand and prevents sending over-rated setpoints if themeasure of fatigue life consumed by a component exceeds a target value,the constraint unit outputting operating set points to the turbine at afrequency greater than the frequency of receipt of optimal set points.

The invention also resides in a wind turbine having a controller asdefined above

The invention further provides a controller for a wind power plant, thepower plant comprising a plurality of wind turbine generators eachhaving a plurality of components, and comprising a plurality of furthercomponents between the wind turbine generator and a grid connection, thecontroller comprising a wind turbine lifetime usage estimator forestimating a measure of the fatigue life consumed by the components, anda wind power plant lifetime usage estimator for estimating a measure offatigue life consumed by the plurality of further components.

Embodiments of the invention will now be described, by way of exampleonly, and with reference to the accompanying drawings, in which:

FIG. 1 is a schematic view of a known wind power plant control regimeusing a power plant controller;

FIG. 2 is a graph of wind speed against power showing a power curve fora typical wind turbine;

FIG. 3 is a schematic view of a wind power plant control regimeembodying the present invention;

FIG. 4 is a similar view to FIG. 3 showing a refinement of the controlregime;

FIG. 5 is a similar view to FIG. 3 showing a further refinement of thecontrol regime;

FIG. 6 is a schematic view of a power plant set point controller;

FIG. 7 is a graph of torque against speed showing operating constraintsfor a wind turbine;

FIG. 8 is a graph illustrating the use of slope control in over-rating;and

FIG. 9 is a graph illustrating the use of offset control in over-rating;

FIGS. 10 a)-d) illustrate the relationship between fatigue andover-rating;

FIG. 11 illustrates a turbine optimiser; and

FIG. 12 illustrates how over-rating is prevented when the lifetime usageof a component exceeds its design limit at a given component age

The following description addresses the general control of turbines in awind turbine power plant, the control of output power from thoseturbines, and the optimisation of operating parameters such as speed andtorque within individual turbines based on set points provided from thepower plant controller. It describes control regimes which are bothdevised by a multi-turbine controller and sent as commands to individualturbines, and control regimes which are implemented by individualturbines and then communicated to a multi-turbine controller such as apower plant controller.

FIG. 1 shows, schematically, a conventional wind power plant 10comprising a plurality of wind turbines 20 each of which communicateswith a power plant controller PPC 30. The PPC 30 can communicatebi-directionally with each turbine. The turbines output power to a gridconnection point 40 as illustrated by the thick line 50.

In operation, and assuming that wind conditions permit, each of the windturbines 20 will output maximum active power up to their nominal setpoint. This is their rated power as specified by the manufacturer. Thepower that is output to the grid connection point is simply the sum ofthe outputs of each of the turbines.

FIG. 2 illustrates a conventional power curve 55 of a wind turbineplotting wind speed on the x axis against power output on the y axis.Curve 55 is the normal power curve for the wind turbine and defines thepower output by the wind turbine generator as a function of wind speed.As is well known in the art, the wind turbine starts to generate powerat a cut in wind speed Vmin. The turbine then operates under part load(also known as partial load) conditions until the rated wind speed isreached at point Vr. At the rated wind speed at point Vr. the rated (ornominal) generator power is reached and the turbine is operating underfull load. The cut in wind speed in a typical wind turbine is 3 m/s andthe rated wind speed is 12 m/s. Point Vmax is the cut out wind speedwhich is the highest wind speed at which the wind turbine may beoperated while delivering power. At wind speeds equal to and above thecut out wind speed the wind turbine is shut down for safety reasons, inparticular to reduce the loads acting on the wind turbine.

As described above, the rated power of a wind turbine is defined in IEC61400 as the maximum continuous electrical power output which a windturbine is designed to achieve under normal operating and externalconditions. Therefore, a conventional wind turbine is designed tooperate at the rated power so that the design loads of components arenot exceeded and that the fatigue life of components is not exceeded.

As shown in FIG. 2, in embodiments of the invention the turbine iscontrolled such that it can produce more power than the rated power asindicated by shaded area 58. The term “over-rating” is understood tomean producing more than the rated active power during full loadoperation. When the turbine is over-rated, the turbine is run moreaggressive than normal and the generator has a power output which ishigher than the rated power for a given wind speed.

The over-rating is characterised by a transient behaviour. When aturbine is over-rated it may be for as short as a few seconds, or for anextended period of time if the wind conditions and the fatigue life ofthe components are favourable to over-rating.

The over-rating power level may be up to 30% above the rated poweroutput.

The PPC controller 30 is shown schematically for ease of illustration.It communicates with each of the turbines and can receive data from theturbines, such as pitch angle, rotor speed, power output etc. and cansend commands to individual turbines, such as set points for pitchangle, rotor speed, power output etc. The PPC 30 also receives commandsfrom the grid, for example from the grid operator to boost or reduceactive or reactive power output in response to demand or a fault on thegrid. Although not shown in the schematic figure, each wind turbine alsohas its own controller which is responsible for operation of the turbineand communicates with the PPC 30.

The PPC controller 30 receives power output data from each of theturbines and is therefore aware of the active and reactive power outputby each turbine and by the plant as a whole at the grid connection point40. If required, the PPC controller 30 can receive an operating setpoint for the power plant as a whole and divide this among each of theturbines so that the output does not exceed the operator assigned setpoint. This power plant set point may be anywhere from 0 up to the ratedpower output for the plant. The “rated power” or “nominal power” outputfor the plant is the sum of the rated power output of the individualturbines in the plant. The power plant set point may even be above therated power output of the plant, i.e. the whole plant is over-rated.This is discussed further below.

FIG. 3 shows a first embodiment of the invention. Instead of receivingan input directly from the grid connection, the power plant controller30 receives a signal which is a measure of the difference between thetotal power plant output and the nominal power plant output. Thisdifference is used to provide the basis for over-rating by individualturbines. In this embodiment, which is only one example, the actualoutput of the power park is subtracted from the nominal or rated outputof the power park at subtractor 60. The difference, shown as errorsignal e in FIG. 3 is input to an integrator 70. The integrator includesin-built saturation which prevents integral wind up which is awell-known problem in controllers where a large change in set pointoccurs and the integral terms accumulate a significant error during therise (wind up), thus overshooting and continuing to increase as thisaccumulated error is offset by errors in the other direction (unwound).

The output from integrator 70 is input to an amplifier 80 which appliesa fixed gain G which scales the integrator output to provide anover-rating amount which is then provided to the controller 30 and sentby the controller to each of the turbines 20. In theory, only a singleturbine may be over-rated, but it is preferred to over-rate a pluralityof the turbines, and most preferred to send the over-rating signal toall the turbines. The over-rating signal sent to each turbine is not afixed control but an indication of a maximum amount of over-rating thateach turbine may perform. Each turbine has an optimiser, which may belocated at the turbine or centrally, and which is described in detailbelow, which will determine whether the turbine can respond to theover-rating signal and, if so, by what amount. For example, where theoptimiser determines that conditions at a given turbine are favourableand above rated wind speed it may respond positively and the giventurbine is over-rated. As the optimisers implement the over-ratingsignal, the output of the power plant will rise and so the error signalproduced by the subtractor 60 will decrease. The integrator will reachequilibrium as the error either reaches zero or the integratorsaturates.

Thus, in this embodiment an over-rating signal is generated. This signalis indicative of the amount of over-rating that may be performed byturbines of the power plant as a whole. However, each turbine respondsindividually to the over-rating signal in accordance with its optimiser.If conditions are such that the total optimisation results inover-rating that threatens to exceed the power plant nominal output, thedifference will reduce and individual optimisers will reduce the amountof over-rating applied.

FIG. 4 shows a modification of the arrangement of FIG. 3. The FIG. 4arrangement takes into account communications delays which may occur ina real power plant between the PPC 30 and the turbines 20. This isimportant as the over-rating signal is communicated from PPC 30 to theturbines 20. If the value tmG is too large, where t is delay time, m isthe ratio of change in over-rating request to power plant output changeand G is the basic feedback gain, the system will overshoot, oscillateor become unstable. This value is a measure of the time taken for theturbines to react to over-rating commands from the PPC 30. To ensurethat tmG is maintained within an acceptable range an upper bound may beplaced on t and m when calculating the maximum feedback gain. However,this approach makes the controller slow to respond to changes in powerplant output. This is undesirable when the output is too low and isunacceptable when the output is too high as such operation could lead tocomponent damage.

The arrangement of FIG. 4 overcomes this problem. The individualturbines are interrogated via their respective controllers by the PPC 30to calculate the value of m. The arrangement of FIG. 4 is similar toFIG. 3 except that the gain of amplifier 85 is expressed as G/m and aninput 100 from the turbines to the amplifier is shown. The delay betweenthe PPC 30 and the turbines 20 is illustrated as delay 90. Thus the onlyparameter that is determined from the upper bound is t. This approachenables the controller to respond more quickly to changes in power plantoutput.

In this example, as with the FIG. 3 example, the over-rating commandsent to each turbine is the same.

It will be appreciated that the basic approach of FIG. 3 may be usedwhere the delay between the controller 30 and the turbines isnegligible. In practice, the delay will be determined by a number offactors but the proximity of the PPC 30 to the turbines will play alarge part in determining the delay. At present a PPC can poll allturbines in a large power plant in about 20 seconds but it isanticipated that this time will reduce to less than 1 second or even 10s of milliseconds in the near future.

In the previous two examples, the same over-rating set point signal issent to each turbine using the total power plant output to provide acontrol input. In the embodiment of FIG. 5, each turbine is given itsown over-rating amount. Thus in FIG. 5 a central optimiser 110 providesan input into the PPC 30. The central optimiser 110 receives an input120 from each turbine which indicates the over-rating capability of thatturbine. That input will depend on a variety of factors such as thelocal wind conditions, the present cost of electricity generated and theage or fatigue damage of the turbine and will be provided by theindividual turbine controller. The central optimiser 110 will calculatean over-rating value for each turbine and communicate that value to eachturbine based on the present over-rating capability of the turbine. Ofcourse the PPC 30 will take other factors into account, such as the needto ensure that the total power output does not exceed the rated outputfor the power plant. The optimiser will base its decisions on the effectof its actions on the fatigue damage of the turbine components and, inFIG. 5, this is performed centrally for all turbines.

Thus FIGS. 3 to 5 illustrate ways in which the over-rating of eachturbine may be implemented via a power plant controller either bygenerating a common over-rating command for each turbine or bygenerating an individual over-rating command for each turbine.

The examples given above each enable the power plant output set point tobe tracked, which in turn makes it possible to vary that power plant setpoint. FIG. 6 illustrates an additional, optional level of control forcontrolling this power plant set point. This controller introduces apower plant set-point controller PPSC which produces set points basedeither on the value of power that can be generated which will depend,for example, on the time of day and year, or on some other externalvariable such as the age of the turbine or the need for the turbineoperator to generate cash flow In this example, each turbine may controlits own fatigue life through an individual turbine optimiser or controlof fatigue may be through a central optimiser as in the example of FIG.5. In FIG. 6, PPST is a power plant set point tracker and corresponds tothe optimiser of FIG. 5.

In a first variant of the FIG. 6 embodiment, the power plant output setpoint is manually moved, or scheduled to move depending on the date.Over the course of a year, a number of set point changes may bescheduled. The purpose of this is to benefit from feed-in tariffs orpower purchase agreements and aids the power plant operator's netpresent value. In addition to seasonal variations, day-night variationsin set points may be scheduled to take into account higher day timeelectricity prices. These are examples only and more advanced variationsin electricity prices may be scheduled in a similar manner to help thepower park operator maximise their return from the turbines.

As well as daily fluctuations in electricity prices there are slowerprices observed due to wider market effects such as the prices of rawmaterials such as oil and gas. Merely scheduling changes in power plantoperating set points does not take these changes into account as theyare not cyclical or necessarily predictable. Instead, the real timeprice of electricity on the spot market for the geographical area to besupplied by the power plant can provide an additional or an alternativeinput to the controller. Thus the power set point is higher when the oilor gas price is above a threshold value and lower when the oil or gasprice falls below that threshold. The turbines are commanded toover-rate if their local controllers will permit it when the set pointis higher so that the power plant operator can benefit from the higherprices in the spot market. It is likely that this approach will have nooverall effect on fatigue lifetimes as the median point for the setpoint is chosen such that the turbines will spend as much time at thehigher set point as at the lower set point.

In addition or alternately to control based on spot market prices, thecontroller may take into account the cost of electricity being traded onthe forwards markets which give a strong indication of the likely priceof electricity some hours, days or even weeks in the future. Thesemarkets are partially driven by load forecasting which takes intoaccount, for example, expected weather conditions and may be used as aninput to the controller to assist in reaching the optimal set-point tocontrol.

In the embodiments described, the output of turbines is over-rated asthe total output of the power plant is below the nominal output of theplant. This could be for a variety of reasons. For example, if the totalrated output of all turbines is equal to the rated output of the powerplant, over-rating may be used if some turbines are shut down formaintenance or are not operating at rated power, for example because thelocal wind conditions do not permit it.

Alternatively, the power plant may be designed to have a rated poweroutput that is higher than the sum of the rated outputs of all theturbines. This is advantageous as over-rating may then be used even whenall turbines are at rated output. This enables the plant operator easilyto take advantage of changes in operating tariffs as described above.The approach outlined above with respect to FIG. 6 enables the powerplant operator to benefit from favourable market conditions and tariffsby using over-rating and so boosting income generated from the powerplant. The operator may choose to use this embodiment of the inventionto over-rate at any time when additional revenue is required, even ifthe market data or the tariffs are not particularly favourable at thetime. The embodiment gives the operator the ability to generateadditional cash-flow which may be required for a variety of businessreasons.

The embodiment described with respect to FIGS. 3 to 6 shows howover-rating can be used to boost the output of individual turbines inresponse to a detected shortfall in power plant output or in response toexternal economic conditions. FIGS. 7-9 are concerned with the actualoptimisation of turbines for over-rating operation, and show how theover-rating command may be implemented.

FIG. 7 is a graph of generator torque against generator rotational speedfor a wind turbine. Curves P₁ and P₂ are lines of constant powercorresponding to power set-points P₁, P₂. They are curved as power isthe product of torque and rotational speed. An over-rating command fromthe PPC 30 takes the form of a shift in the power set-point to a newvalue. The turbine must then select an operating speed and torque todeliver that power.

A turbine has hard constraints defined as the maximum and minimum torqueand speed at which it can operate. These constraints are imposed by thecontroller and dictated by factors such as noise limits, gearboxlubrication, component lifetime etc. These constraints are referred toas hard constraints as the controller may not violate them except in theextreme case of performing a shutdown. Although these constraints arerigid, they may vary over time.

The controller may also impose soft constraints which are intended toprevent the turbine shutting down during over-rating, typically asthermal limits or maximum generator speed are approached. A temperatureincrease in key components will occur during over-rating, for examplethroughout the drive train, and could trip shutdown. Soft constraintsmay be lower than hard constraints but result in the controller reducingthe amount of over-rating rather than performing a shut down. Thus theturbine optimiser may include soft constraint values for drive trainrelated parameters and generator speed. When the controller detects thata measured value is approaching a soft constraint value the over-ratingsignal is reduced.

Thus, referring to FIG. 7, on the graph of Torque against RotationalSpeed there is a box 200 within which the turbine can operate. The boxis bounded by maximum and minimum speed and torque. The purpose of theturbine optimiser is to choose the optimum operating point for theturbine. In FIG. 5 the optimiser is shown as a central unit whichperforms calculations for a plurality of turbines, possibly all turbinesof the power plant. This need not be the case and the optimiser can beperformed on a computer physically located at a turbine, for example, aspart of the existing turbine controller. In that case, the data ispassed over the communications link to the PPC 30. The term ‘turbineoptimiser’ therefore refers to the selection of set points for a giventurbine rather than implying any location.

It can be seen from FIG. 7 that the turbine cannot achieve operation atany point on constant power curve P₁, which is, at all times outside thebox 200. In this case, if the PPC 30 requests a power set point P₁ at agiven turbine, the turbine optimiser will select the optimal rotationalspeed and torque at the top right hand corner 210 of the box. If the PPC30 requests a power set point P2 at a given turbine, the line ofconstant power P₂ passes through the box, and so any point on that partof the line that passes through the box could be chosen as the operatingpoint. The purpose of the turbine optimiser is to choose the best pointalong this part of the curve. Although the figure shows generatorrotational speed, the term rotational speed may refer to the rotationalspeed of the generator, the rotor or the speed anywhere along the drivetrain. Although the absolute values are different, they are all related.

Although not shown in the figure, if a constant power curve were to gocompletely below the box 200 there are two available choices. Firstly,the turbine shuts down as any set-points within the box would produce apower output above the power set point. Secondly, the turbine sets therotational speed and torque as the bottom left corner of the box 200, byanalogy to the case with curve P₁, and advises the power plantcontroller 30 that it is running above the requested power set point.The PPC 30 can then optimise the scenario by lowering the set-points forone or more other turbines. However, if all turbines, or a substantialpercentage, were in the bottom left corner, at least some would have tobe shut down.

Any point inside box 200 on the power set-point line is valid. Thefollowing section describes how the set-point (or line of constantpower) is chosen having regard to the fatigue lifetime of the turbineand components of the turbine.

The description above with respect to FIGS. 3 to 5 explained how acommon over-rating signal or set point sent from the PPC 30 is used tocontrol over-rating by all turbines to control the overall power plantoutput. However, over-rating carries inherent risks, particularly to theintegrity of turbine components and it is important to control theextent to which over-rating is used over the lifetime of a turbine. Oneway in which this may be achieved is for each turbine to respond to thecommon over-rating signal or set point in a way that best suits itself.This calculation or assessment may be made either at the individualturbines as part of their central process, or at the PPC 30 which mayperform the calculation individually for multiple turbines based as datareceived from those turbines.

Thus, when the over-rating demand is received at each turbine from thePPC 30, each turbine processes and responds to this signal takingfatigue into account. A turbine may not over-rate or may not over-rateat the level requested if the effect on the fatigue lifetime of criticalcomponents is too great. Examples of critical components include therotor blades, blade pitch systems, main bearing, gearbox, generator,converter, transformer, yaw system, tower and foundations. This willdepend on the conditions at the turbine as well as the lifetime historyof the turbine. For example, a turbine that is near the end of its lifeexpectancy may be highly fatigued and so not suited to run at theover-rating level demanded. If the power plant output is insufficient assome or all of the turbines are operating under the demanded over-ratinglevel for fatigue saving, the over-rating demand will keep rising untilit reaches its set-point or saturates.

Where a feedback system is used, such as in FIGS. 3 and 4, each turbinecan vary its over-rating response according to lifetime usage. Theover-rating set point sent from PPC 30 is processed through a responsefunction, examples of which are described below in FIGS. 8 and 9. Inthese figures the turbine over-rating response is shown on the Y axisand the selected response is then sent to the system that choosesrotational speed and torque as described in the previous section. Thus,in the graph of FIG. 8, a slope control approach is adapted. Here thecontroller has issued a 5% over-rating demand to the turbines. Ideally,the turbine will respond with a 5% over-rating. If the outputstabiliser, which forms part of the controller requires so, the turbinemay respond with the 5% over-rating, over-riding fatigue issues. Ahighly fatigued turbine will de-rate when the request is zero or toslightly over-rate as shown in dashed line 300 in FIG. 8. Low fatiguedturbines may over-rate even when the over-rating request from thecontroller is zero as shown by dashed line 302 in FIG. 8. The slope ofthese lines may vary according to the degree of fatigue that has beenexperienced by the turbine and will affect the value of m, the ratio ofchange in over-rating request to power plant output change describedwith reference to FIG. 4 above.

In FIG. 8, the dashed line 304 passing through the origin represents a1:1 ratio of response to demand that would be provided by a turbine withan expected degree of fatigue.

FIG. 9 shows an alternative approach although it is stressed that FIGS.8 and 9 only show two of a large number of possible approaches. In FIG.9, the axes are the same as in FIG. 8 and dashed line 304 alsorepresents a 1:1 response from a turbine with expected fatigue. However,in this case, as shown by dashed line 306, if the turbine issufficiently highly fatigued it will never over-rate as the functionwill drop below the X axis completely. Similarly, if the fatigue issufficiently low, the turbine will always over-rate. There will be norapid changes in the responses as the plant demand changes as the slopeis constant.

In the description of FIG. 7, the application of hard constraints to thespeed and torque set points was described. These fatigue control softconstraints may be applied before the hard constraints. Thus, the choiceof set point within the box 200 of FIG. 7 is affected by the fatigue orlifetime usage information.

When assessing fatigue of different components of the wind turbines,different components will fatigue at different rates under variousconditions. Some component's fatigue life will be more sensitive tospeed and others will be more sensitive to torque. The turbinecomponents can be split into speed sensitive components and torquesensitive components and the slope and/or position of the line for thetwo response functions of FIGS. 8 and 9 is then chosen according to theworst of each group.

In order to make operation at above rated power less damaging and haveless fatigue damage, the critical components when considering speed andtorque related fatigue damage may be upgraded. For example, if it isestablished that the gearbox is the critical fatigue related component,the gearbox may be upgraded relative to the other components so that theoverall fatigue expectancy falls and it becomes more acceptable toover-rate the turbine and the time for which the turbine can beover-rated increases.

Thus, the embodiments of FIGS. 8 and 9 provide for fatigue controlwithin the context of an over-rating system that is based on feedback.

FIG. 5 described an over-rating approach that was based on directcalculation of over-rating amounts rather than feedback based on adifference signal at the power plant output. Fatigue control may beincorporated into this approach. The PPC 30 is responsible for settingthe set points for each turbine and also chooses power and torque setpoints. By using a state-based system, where the states are theaccumulated fatigue for each turbine, and the inputs are power or speedand torque set points, as similar control based on fatigue may beachieved as the PPC 30 will be aware of fatigue data communicated fromindividual turbines which can then be taken into account when settingthe power or speed and torque set points.

Thus, embodiments of the invention provide a variety of controllerswhich enable wind turbines of a power plant to be over-rated.Over-rating may be by a common control provided in response to ameasured output that is below the power plant nominal output or it maybe by optimisation of individual turbines. Over-rating may additionally,or alternatively, be based on outside economic factors based on thecurrent price of generated power and expected or anticipate changes inthat cost. Moreover, when determining the extent to which turbines canbe over-rated, the fatigue life of turbine components can be taken intoaccount so enabling the lifetime of the turbine to be preserved and,where appropriate, additional revenue to be generated throughover-rating.

The various embodiments described may be combined to provide a systemwhich enables over-rating both to boost output where the power plant isat below nominal output and to take into account external economicfactors such as a controller may also incorporate control based onfatigue lifetimes.

Thus, in the embodiments described, a power plant having a plurality ofwind turbines aims to supply the grid with an amount of power agreedupon in advance. The power plant controller manages how much power isextracted from each turbine in order to match the demand.Conventionally, the power demand sent from the PPC to the individualturbines is restricted by their respective nameplate ratings. In theembodiments described, the turbines restrict their own production andthe power demand from the PPC is sent to a Turbine Optimizer (TO) oneach turbine. This Optimizer is designed to compute and send speed andtorque set-points to the Production Controller. The set-point is chosento maximise the power produced by the turbine across its lifetime whilstkeeping the loads within their design limits. The design limits for aturbine are made up of the fatigue and extreme load limits of all thecomponents that make up a turbine. Alternatively, other set-pointsignals could be sent and in one embodiment of the invention at leastone of power, torque and speed set-points are sent.

To ensure the fatigue load limits of all components remain within theirdesign lifetimes, the loads it experiences (be they bending moments,temperatures, forces or motions for example) may be measured and theamount of component fatigue life consumed calculated, for example usinga well known technique such as a rainflow count and Miner's rule or achemical decay equation. The individual turbines can then be operated insuch a way as to not exceed the design limits. A device for themeasuring of the fatigue life consumed for a given component is referredto as its Lifetime Usage Estimator (LUE). The output from these LUES canbe used in two ways. The LUE can inform the turbine whether the totalfatigue experienced at a given point in time is below or above the levelthe turbine is designed to withstand, and the TO can decide to over-ratewhen the damage is below the expected level. The LUEs can also be usedto measure the rate of accumulation of fatigue, as opposed to anabsolute level. If the fatigue lives of the components are beingconsumed quickly, it may be more prudent to not over-rate the turbineeven if its current fatigue life is less than expected at that time. Therate of usage of fatigue life may then be one input to the over-ratingcontroller and assist in the decision whether or not to over-rate.

In practice it is not appropriate to measure all the load signals on allthe components and instead LUEs are used for a subset of all thecomponents on the turbine. In order to prevent the components whoselifetime used is not measured with an LUE from reaching their fatiguelimits, and also prevent components from exceeding extreme limits,constraints are placed on the turbine operation based upon values ofmeasurable signals (for example temperature or electrical current).These constraints are referred to as operational constraints.Operational constraint controllers (OCCs) define how the turbine'sbehaviour should be restricted in order to prevent the measured signalsfrom exceeding these operational constraints or triggering alarms whichmay result in turbine shutdown. For example in the case of anoperational constraint on the temperature of a bulbar, the operationalconstraint controller may reduce the power reference sent to theProduction Controller by an amount inversely proportional to thedifference between the temperature limit and the current measuredtemperature. Another use for operational constraint controllers could beto restrict the turbine operation based upon the produced noise. Thiscontroller would exploit a model of how an operating point translates toa measure of noise.

In order to prevent the component extreme loads from reaching theirlimits, constraints on the torque, speed and power are defined. This maybe achieved by running simulations offline and judging the operatingpoints that can be achieved without the probability of exceeding anextreme load limit being greater than a pre-determined amount. A moreadvanced way would be to select the limits in terms of the currentenvironmental conditions, for example, if the current wind conditionswere high turbulence, the limits would be lower than if they were lowturbulence. These conditions could be judged using a Liar unit, datafrom a MET mast, or turbine signals.

As described, the extent to which a given turbine over-rates, may, inone aspect of the invention, vary according to the price the operator ispaid for electricity at any given time, thus maximising the income fromthe investment. This aspect may be incorporated with lifetime usageestimation transmitting a measure of production importance with thepower demand and using the measure to relax or restrict limits on rateof accumulation of fatigue or probability of extreme loads exceedingtheir design values. Forecasts of the weather based upon meteorologicaldata may also be exploited to determine when over-rating could be mostvaluable over the prediction horizon.

As is clear from the foregoing description, the decision whether toover-rate may be made by the turbine itself or by a centralisedcontroller. When operating a set of turbines, it may be more prudent tocompare the conditions of the various turbines to decide which onesshould over-rate, by how much and in what way. This may be achieved intwo separate ways. In the first implementation, the lifetime usageestimators and operational constraint controllers still exist on theindividual turbines and these provide the centralized controller withconstraints on torque, speed and power for each turbine in the plant.The centralized controller then performs an optimization to minimize thedeviation of total power produced from the grid demand, and distributethe loads between the turbines in a manner matching their current stateand the environmental conditions they are seeing (or expected to see).This optimization could also exploit information about the turbinelocations and current wind direction in order to minimize the loadsresulting from aerodynamic interaction.

In the second implementation, turbines are allowed to exchangeinformation with a subset (or the whole set) of turbines in the powerplant. The information exchanged would not be the components lifetimesused, rather a certificate relating to its current condition and abilityto produce in the future. This would perform less well than the globaloptimal achieved in the first implementation but would havesignificantly reduced communication and computational demands. Thissystem would mimic the way in which internet routers manage theirtransfer rate based upon the aggregate cost per communication link andthe previous amount of data sent (TCP-IP).

The lifetime usage estimators will now be described in more detail. Thealgorithm required to estimate lifetime usage will vary from componentto component and the lifetime usage estimators comprise a library oflifetime usage estimator algorithms including some or all of thefollowing: load duration, load revolution distribution, rainflowcounting, stress cycle damage, temperature cycle damage, generatorthermal reaction rate, transformer thermal reaction rate and bearingwear. Additionally other algorithms may be used. As mentioned above,lifetime usage estimation may only be used for selected key componentsand the use of a library of algorithms enables a new component to beselected for LUE and the suitable algorithm selected from the libraryand specific parameters set for that component part.

In one embodiment, lifetime usage estimators are implemented for allmajor components of the turbine including the blade structure, bladebearing components, blade pitch system components, main shaft, mainshaft bearing, gearbox, generator, converter, electrical power systemstransformer, nacelle bedplate, yaw system, tower and the foundation. Inany implementation it may be decided to omit one or more of thesecomponents and/or to include further components.

As examples of the appropriate algorithms, rainflow counting may be usedin the blade structure, blade bolts, pitch system, main shaft system,converter, yaw system, tower and foundation estimators. In the bladestructure algorithm, the rainflow count is applied to the blade rootbending flap wise and edgewise moment to identify the stress cycle rangeand mean values and the output is sent to the stress cycle damagealgorithm. For the blade bolts, the rainflow count is applied to thebolt bending moment to identify stress cycle range and mean values andthe output sent to the stress cycle damage algorithm. In the pitchsystem, main shaft system, tower and foundation estimators the rainflowcounting algorithm is also applied to identify the stress cycle rangeand mean values and the output sent to the stress cycle damagealgorithm. The parameters to which the rainflow algorithm is applied mayinclude:

Pitch system—pitch force;Main shaft system—main shaft torque;Tower—tower stress;Foundation—foundation stress.

In the yaw system the rainflow algorithm is applied to the tower toptorsion to identity the load duration and this output is sent to thestress cycle damage algorithm. In the converter, generator power and RPMis used to infer the temperature and rainflow counting is used on thistemperature to identify the temperature cycle and mean values. Theoutput is then sent to the converter damage algorithm.

Lifetime usage in the blade bearings may be monitored either byinputting blade flap wise load and pitch velocity as inputs to the loadduration algorithm or to a bearing wear algorithm. For the gearbox, theload revolution duration is applied to the main shaft torque tocalculate the lifetime used. For the generator, generator RPM is used toinfer generator temperature which is used as an input to the thermalreaction rate generator algorithm. For the transformer, the transformertemperature is inferred from the power and ambient temperature toprovide an input to the transformer thermal reaction rate algorithm.

Where possible it is preferred to use existing sensors to provide theinputs on which the algorithms operate. Thus, for example, it is commonfor wind turbines to measure directly the blade root bending edgewiseand flap wise moment required for the blade structure, blade bearing andblade bolts estimators. For the pitch system, the pressure in a firstchamber of the cylinder may be measured and the pressure in a secondchamber inferred, enabling pitch force to be calculated. These areexamples only and other parameters required as inputs may be measureddirectly or inferred from other available sensor outputs. For someparameters, it may be advantageous to use additional sensors if a valuecannot be inferred with sufficient accuracy.

The algorithms used for the various types of fatigue estimation areknown and may be found in the following standards and texts:

Load Revolution Distribution and Load Duration:

-   -   Guidelines for the Certification of Wind Turbines, Germainischer        Lloyd, Section 7.4.3.2 Fatigue Loads

Rainflow:

-   -   IEC 61400-1 ‘Wind turbines—Part 1: Design requirements, Annex G

Miners Summation:

-   -   IEC 61400-1 ‘Wind turbines—Part 1: Design requirements, Annex G        Power Law (Chemical decay):    -   IEC 60076-12 ‘Power Transformers—Part 12: Loading guide for        dry-type power transformers’, Section 5

The frequency with which lifetime usage is calculated may vary. In oneembodiment, the lifetime of a component that has been used is calculatedevery few minutes and expressed in years. The rate of lifetime usage maybe calculated every minute. However, other time intervals may be used.The calculated values are provided to the turbine optimiser whichtherefore receives values for all major components every few minutes andusage rate values for all major components every minute.

The turbine optimizer is illustrated in FIG. 11. The turbine optimizeroperates the turbine at a power level that does not exceed that sent bythe PPC and outputs the optimal level of torque and speed based oninformation from the lifetime usage estimator and the OCC.

As can be seen from FIG. 11, the turbine optimiser 400 includes aset-point selector 410 and a fast constraint satisfaction unit 420. Theset-point selector receives as its inputs the PPC demand, operationalconstraints from the OCC and the lifetime usage data for the majorcomponents as described above. In the FIG. 11 example the input is theabsolute value of lifetime usage rather than the rate of usage. Theset-point selector outputs optimal set-points to the fast constraintsatisfactions unit periodically, for example between every minute andevery few minutes. The fast constraint satisfaction unit 420 alsoreceives as inputs the PCC demand signal, the lifetime usage date andthe operating constraints and outputs speed and torque set pointsperiodically. In the example shown, set-points are output at thefrequency of demand signals received from the PPC.

Of the components for which lifetime usage is determined, each will beclassified as speed sensitive if the damage accumulated correlates withspeed over-rating percentage only and torque sensitive if the damageaccumulated correlates with the torque over-rating percentage only.Components may be generic is they are sensitive to both torque andspeed.

As mentioned, the set point selector 410 chooses the optimal speed andtorque set-points. This is done on a slow time scale Ts which is in theorder of minutes. The Set-Point Selector update rate Ts, is chosen tomaximise performance whilst ensuring the Over-rating controller does notinterfere with existing controllers in the turbine software.

The set-point selector 410 receives the lifetime usage estimates for allestimated components and selects the value corresponding to the mostdamaged component; that with the greatest used life. If that componenthas consumed more of its fatigue life than it has been designed to haveused at that point in time (the Set-Point Selector outputs Optimal speedand power Set-Points equal to their respective rated values. Thus, inthat circumstance there is no over-rating. This is illustrated in FIG.12 which shows the design fatigue level as a straight line graph oftime, measure from installation to decommission dates, against lifetimeusage estimate. In the figure the accumulated fatigue at a time ‘today’is greater than the design level and so no over-rating would bepermitted. FIG. 12 is schematic only and a straight line graph may notreflect the desired accumulation of lifetime and the rate of usage willdepend on the season.

If any of the speed sensitive components have used more of their fatiguelives than their design value at that point in time, the Set-pointselector outputs an Optimal speed Set-point equal to rated speed and ifany of the torque sensitive components have used more of their fatiguelives than their design value at that point in time, the Set-pointselector outputs an Optimal torque Set-point equal to rated torque. TheSet-Point Selector chooses an Optimal Set-Point to maximise the powerproduced subject to constraints from the PPC and Operational ConstraintControllers sampled at the beginning of the time-step. The Set-PointSelector also attempts to equalize the damage to the most damaged speedand torque sensitive components.

The Fast constraint satisfaction unit in this example operates at ahigher frequency than the set-point selector and applies saturations tothe Optimal speed and torque Set-Points, limiting the outputs to thelimits provided by the Operational Constraint Controllers and PPC.

The Fast constraint satisfaction block does not allow the TurbineOptimiser to send set-points over-rated by speed/torque if any of thespeed/torque sensitive components have consumed more than their targetlife. Similarly, the Turbine Optimiser will not send an over-rated powerset-point if any of the generic components have consumed more than theirtarget life.

The embodiments described contemplate over-rating based on torque andspeed. Over-rating may also be used in constant speed turbines, forexample constant speed active stall turbines. In this case, only thepower signal is over-rated and each turbine in the power plant, or eachturbine in a subset of the power plant, sends an over-rating demand tothe PPC which monitors the total output and reduces the amount ofover-rating if the total output is above the rated output of the powerplant. Alternatively only the power signal may be over-rated. Inpractice, this is likely to be rarely necessary as, dependent on weatherconditions, not all turbines will be over-rating and some may not begenerating any power, for example as they are shut down for maintenance.Alternatively, a power regulation model uses a control loop whichcompares wind speed input data from each turbine to known power curvesto predict how much power each turbine can produce at any given time.The PRM sends individual power demands to each turbine with theobjective to obtain as close to power plant rated power as possible. ThePRM may be used with an extended power curve for an over-rated turbine.

Embodiments of the invention enable the use of over-rating at suitabletimes to lower the cost of energy. Within a wind power plant,over-rating may be used selectively taking into account variations inwind and site conditions across the wind park, variations in rates ofcomponent wear and tear, turbine shut-downs for maintenance or faultsand variations in the price of electricity. As turbine componentsfatigue at different rates in different conditions, the actual lifetimeof some components may be considerably more than the 20 year expectedlifetime for a wind turbine. In a given set of conditions, thecomponents that are closest to their aggregate lifetime limit could havea low instantaneous fatigue rate. As other components have a longerlifetime as these are not driving the overall turbine life, the turbinehas spare production capacity. In addition, different turbines in thepower plant will experience different conditions over their life.

Thus any turbine may be over-rated, if the conditions allow it, tomaximise energy output while maintaining the turbine lifetime. This isillustrated by FIG. 10 a)-d). FIG. 10 a) shows the total lifetimefatigue of various components. Component 5 is the most critical,defining its 20 year life. FIG. 10 b) shows an example of conditionswhere the instantaneous fatigue rate of component 5 is lower that its 20year average and that of component 7 is greater than its average. FIG.10 c) shows that under these conditions the turbine can be over-rated,bringing component 2, which is now the one with the highest fatiguerate, up to its 20 year limit. FIG. 10 d) shows that the turbine can beover-rated even more if component 2 is allowed to fatigue at a rate thatis higher than its lifetime limit. At this level of over-rating thecomponent would fail before the end of its 20 year lifetime but this isnot a problem for short periods of time as the component has lifetimetotal fatigue to spare. This maximal over-rating is therefore limited byaccumulated fatigue rather than instantaneous fatigue. In this case,component 7 does not have spare lifetime capacity and so does not passits 20 year limit. Thus, the turbines can reduce the variability of thepower plant output by acting as a group

Lifetime usage estimators have been described in conjunction with windturbine components in the control of over-rating in wind turbines.However, lifetime usage estimators may also be used in other parts of awind turbine power plant. The wind turbine generator level comprises theplurality of wind turbine and controllers described above. The powerplant level comprises other power plant components between the windturbine generators and the point of connection to the grid and includesthe substation transformer and cabling between the turbines and thesubstation transformer and between the grid and the substationtransformer. Lifetime usage estimators can be used on these componentsto provide inputs to a power plant optimiser in a similar manner to theturbine optimiser described above.

Many alternatives to the embodiments described are possible and willoccur to those skilled in the art without departing from the scope ofthe invention which is defined by the following claims.

What is claimed is:
 1. A controller for a wind turbine, comprising aturbine optimiser and a lifetime usage estimator, the turbine optimiseroutputting setpoints for operating parameters of the wind turbine basedon a power demand input and an input from the lifetime usage estimator,wherein the lifetime usage estimator calculates a measure of the fatiguelife consumed by each of a plurality of turbine components based on alifetime usage algorithm for each component, the lifetime usagealgorithms operating on values of variables affecting the fatiguelifetime of the components, the values being obtained from or derivedfrom sensors on the wind turbine.
 2. A controller according to claim 1,wherein the setpoints output by the turbine optimiser are at least oneof power, torque and speed setpoints.
 3. A controller according to claim1, wherein the wind turbine is a constant speed turbine and thesetpoints output by the optimiser are active power setpoints.
 4. Acontroller according to claim 1, wherein the input from the lifetimeusage estimator comprises a measurement of fatigue life consumed by eachcomponent.
 5. A controller according to claim 1, wherein the input fromthe lifetime usage estimator comprises a measurement of rate of usage offatigue life by each component.
 6. A controller according to any ofclaim 1, wherein the controller is an over-rating controller.
 7. Acontroller according to any of claim 1, wherein the turbine optimisercompares the proportion of the fatigue life consumed by the componentswith a target consumption based on the age of the component and preventsover-rating of the turbine if the fatigue life consumed by any componentis greater than the target consumption for that component.
 8. Acontroller according to any of claim 1, wherein the turbine optimisercompares the proportion of the fatigue life consumed by the most damagedcomponents with a target consumption based on the age of that componentand prevents over-rating of the turbine if the fatigue life consumed isgreater than the target consumption for that component.
 9. A controlleraccording to claim 7, wherein the over-rating comprises speed and torqueover-rating and wherein the fatigue life of each component is speedsensitive, torque sensitive or speed and torque sensitive, whereinover-rating of the speed is prohibited if the consumption of fatiguelife by a speed sensitive component exceeds the target consumption,over-rating of torque is prohibited if the consumption of fatigue lifeby a torque sensitive component exceeds the target consumption andover-rating of speed and torque is prohibited if the consumption offatigue life by a torque and speed component exceeds the idealconsumption.
 10. A controller according to claim 1, wherein the turbineoptimiser communicates with a wind power plant controller and receivesthe power demand input from the wind power plant controller.
 11. Acontroller according to claim 1, wherein the controller is located atthe wind turbine for which it generates setpoint signals.
 12. Acontroller according to claim 1, comprising a store storing a library oflifetime usage algorithms, wherein the lifetime usage of each of thecomponents is calculated using at least one of the algorithms.
 13. Acontroller according to claim 1, wherein the turbine components compriseone or more of blade components, blade bearing components, blade pitchsystem components, main shaft, main shaft bearing, gearbox, generator,converter, electrical power systems transformer, nacelle bedplate, yawsystem, tower or foundation.
 14. A controller according to claim 13,wherein the blade components comprise blade structure and blade bolts.15. A controller according to claim 1, wherein the lifetime usagealgorithm for a component estimates a stress cycle range and mean valuebased on received input values.
 16. A controller according to claim 15,wherein the stress cycle and mean values are inputs to a stress cycledamage algorithm which provides as its output, the measure onconsumption of fatigue life.
 17. A controller according to claim 1,wherein the turbine optimiser receives an input of operationalconstraints indicating restrictions on operating parameters of theturbine.
 18. A controller according to claim 1, wherein the turbineoptimiser comprises a set point selector which receives the power demandinput and the measure of lifetime consumed for the components andperiodically calculates optimal operating set points for the turbineparameters, and a constraint unit which receives the optimal set points,the measure of lifetime usage and the power demand and prevents sendingover-rated setpoints if the measure of fatigue life consumed by acomponent exceeds a target value, the constraint unit outputtingoperating set points to the turbine at a frequency greater than thefrequency of receipt of optimal set points.
 19. A controller accordingto claim 1, wherein the setpoints output by the turbine optimiser arefurther based on operational constraints input to the turbine optimiser.20. A wind turbine comprising a controller according to claim
 1. 21. Amethod of controlling a wind turbine, comprising obtaining values ofvariables affecting the fatigue lifetime of one or more components ofthe turbine from turbine sensors, applying a fatigue lifetime usagealgorithm to the variables to determine a measure of the fatigue lifeconsumed by each of a plurality of turbine components, inputting themeasures of fatigue life consumption and a power demand to a turbineoptimiser, and generating setpoints for operating parameters of the windturbine by the turbine optimiser.
 22. A method according to claim 21,wherein the setpoints output by the turbine optimiser are at least oneof power, torque and speed setpoints.
 23. A method according to claim21, wherein the wind turbine is a constant speed turbine and thesetpoints output by the optimiser are active power setpoints.
 24. Amethod according to claim 21, wherein the measure of fatigue lifecomprises an estimate of fatigue life consumed by each component.
 25. Amethod according to claim 21, wherein the measure of fatigue lifecomprises a measurement of rate of usage of fatigue life by eachcomponent.
 26. A method according to claim 21, wherein the controller isan over-rating controller.
 27. A method according to claim 21, whereinthe turbine optimiser compares the proportion of the fatigue lifeconsumed by the components with a target consumption based on the age ofthe component and prevents over-rating of the turbine if the fatiguelife consumed by any component is greater than the target consumptionfor that component.
 28. A method according to claim 21, wherein theturbine optimiser compares the proportion of the fatigue life consumedby the most damaged component with a target consumption based on the ageof that component and prevents over-rating of the turbine if the fatiguelife consumed is greater than the target consumption for that component.29. A method according to claim 27, wherein the over-rating comprisesspeed and torque over-rating and wherein the fatigue life of eachcomponent is speed sensitive, torque sensitive or speed and torquesensitive, wherein over-rating of the speed is prohibited if theconsumption of fatigue life by a speed sensitive component exceeds atarget consumption, over-rating of torque is prohibited if theconsumption of fatigue life by a torque sensitive component exceeds theideal consumption, and over-rating of speed and torque is prohibited ifthe consumption of fatigue life by a torque and speed component exceedsthe target consumption.
 30. A method according to claim 21, wherein theturbine optimiser communicates with a wind power plant controller andreceives the power demand input from the wind power plant controller.31. A method according to claim 21, wherein the controller is located atthe wind turbine for which it generates setpoint signals.
 32. A methodaccording to claim 21, wherein a library of lifetime usage algorithms isstored at the turbine, wherein the lifetime usage of each of thecomponents is calculated using at least one of the algorithms.
 33. Amethod according to claim 21, wherein the turbine components compriseone or more of blade components, blade bearing components, blade pitchsystem components, main shaft, main shaft bearing, gearbox, generator,converter, electrical power systems transformer, nacelle bedplate, yawsystem, tower or foundation.
 34. A method according to claim 33, whereinthe blade components comprise blade structure and blade bolts.
 35. Amethod according to claim 21, wherein the lifetime usage algorithm for acomponent estimates a stress cycle range and mean value based onreceived input values.
 36. A method according to claim 35, wherein thestress cycle and mean values are inputs to a stress cycle damagealgorithm which provides as its output, the measure on consumption offatigue life.
 37. A method according to 21, wherein the turbineoptimiser receives an input of operational constraints indicatingrestrictions on operating parameters of the turbine.
 38. A methodaccording to 21, wherein the setpoints generated by the turbineoptimiser are further based on operational constraints input to theturbine optimiser.
 39. A method according to claim 21, wherein theturbine optimiser periodically calculates optimal operating set pointsfor the turbine parameters on the basis of received power demand inputsand the measure of lifetime consumed for the components, and preventssending over-rated setpoints if the measure of fatigue life consumed bya component exceeds a target value.
 40. A method of over-rating a windturbine comprising receiving an over-rating demand signal from a powerplant controller, determining a measure of the fatigue life consumed byeach of a plurality of turbine components based on a lifetime usagealgorithm for each component and sensed parameter values for thatcomponent, generating and sending to the turbine at least one of a powera torque set point in accordance with the over-rating demand set point,wherein the over-rating set points are not sent to the turbine if themeasure of the fatigue life consumed exceeds a target value for acomponent.
 41. A controller for a wind power plant, the power plantcomprising a plurality of wind turbine generators each having aplurality of components, and comprising a plurality of furthercomponents between the wind turbine generator and a grid connection, thecontroller comprising a wind turbine lifetime usage estimator forestimating a measure of the fatigue life consumed by the components, anda wind power plant lifetime usage estimator for estimating a measure offatigue life consumed by the plurality of further components.